class_docstr: Speaker Diarization model
  
  If "attractor" is "None", SA-EEND will be used.
  Else if "attractor" is not "None", EEND-EDA will be used.
  For the details about SA-EEND and EEND-EDA, refer to the following papers:
  SA-EEND: https://arxiv.org/pdf/1909.06247.pdf
  EEND-EDA: https://arxiv.org/pdf/2005.09921.pdf, https://arxiv.org/pdf/2106.10654.pdf
code: def collect_feats(
      self,
      speech: torch.Tensor,
      speech_lengths: torch.Tensor,
      spk_labels: torch.Tensor = None,
      spk_labels_lengths: torch.Tensor = None,
      **kwargs,
  ) -> Dict[str, torch.Tensor]:
      feats, feats_lengths = self._extract_feats(speech, speech_lengths)
      return {"feats": feats, "feats_lengths": feats_lengths}
  
imports: from contextlib import contextmanager
  from itertools import permutations
  from typing import Dict, Optional, Tuple
  import numpy
  import torch
  import torch.nn.functional
  from packaging.version import parse
  from typeguard import check_argument_types
  from espnet2.asr.encoder.abs_encoder import AbsEncoder
  from espnet2.asr.frontend.abs_frontend import AbsFrontend
  from espnet2.asr.specaug.abs_specaug import AbsSpecAug
  from espnet2.diar.attractor.abs_attractor import AbsAttractor
  from espnet2.diar.decoder.abs_decoder import AbsDecoder
  from espnet2.layers.abs_normalize import AbsNormalize
  from espnet2.torch_utils.device_funcs import force_gatherable
  from espnet2.train.abs_espnet_model import AbsESPnetModel
  from espnet.nets.pytorch_backend.nets_utils import to_device
label_desc: Use image node_id 3 for calling the ESPnetDiarizationModel obj's underlying member method code with example usage: obj.collect_feats(speech, speech_lengths, spk_labels, spk_labels_lengths) and returns: dict